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> The best large-scale work I’ve seen finds the average association between overall screen/social-media use and teen well-being is tiny

Can you share the source? The last time I looked the association was both clear and pretty strong, e.g. "There was a linear dose–response association of TSSM and risk of depression. The risk of depression increased by 13% (OR = 1.13, 95%CI: 1.09 to 1.17, p < 0.001) for each hour increase in social media use in adolescents." DOI:10.3390/ijerph19095164

> “Social media” lumps together very different things

HN does this, but the research is usually pretty clear in spelling out they mean FB, Insta, TikTok and so on.

> If you want a lever that actually changes incentives, go after business model & design

I too would like changes in that direction (mostly because adults are also affected negatively by social media), but keep in mind even a non-optimized, strictly chronological feed produces these negative effects, see keyword (and associated studies for) "upward social comparison", i.e. people are always more inclined to post about things that went well or are fun, and thus even a pure chronologically sorted feed produces a warped perception of normal social reality.

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Yep — what I had in mind was Orben & Przybylski, Nature Human Behaviour (2019). They use a specification curve analysis across three large datasets (total n ≈ 355k) and find the association between digital technology use and adolescent well-being is negative but very small (explains ≤ 0.4% of the variance). DOI: 10.1038/s41562-018-0506-1.

I think your cited meta-analysis (Liu et al., IJERPH 2022; DOI: 10.3390/ijerph19095164) is useful too — but it’s answering a narrower question (depression specifically, “time spent on social media” specifically) and it’s based on 5 studies with high heterogeneity, mostly observational/self-report. The paper itself basically says “interpret with caution; motivation/content/engagement matter.” So I don’t read these as contradictory so much as “big-average effect looks small, but there may be real effects for specific outcomes / subgroups / behaviors.”

Also worth noting newer longitudinal work is starting to tease directionality: e.g. Nagata et al., JAMA Network Open (2025) (ABCD cohort) finds higher social media use predicts later depressive symptoms more than the reverse. That to me is the strongest argument that “time” isn’t purely a proxy for pre-existing depression.

On “social media lumps together”: agreed research often names Facebook / Instagram / TikTok etc — but even within one platform, the mechanism differs a ton (DMs / group chat / creating vs passive feed consumption + ads + notifications). That’s why I’m more bullish on regulating business model + engagement features than banning the category.

And on “upward social comparison”: totally agree it’s not “solved” by chronological feeds. People curate; viewers compare. There’s direct work on the “everyone else is happier” perception effect on Facebook (Chou & Edge 2012, DOI: 10.1089/cyber.2011.0324) and adolescent social-comparison/feedback-seeking being linked with depressive symptoms (Nesi & Prinstein 2015). My claim is just that algorithmic ranking + push/autoplay/infinite scroll likely makes that dynamic more frequent/intense, so design changes still buy meaningful leverage even if they don’t make the risk zero.

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Thanks for the reply, will look into the links. And yes, full agreement, algorithmic ranking is a whole different dynamic, and has to be both researched and regulated differently than a dumb feed. Even the latter probably has levers moderating human reception, i.e. if we evolved in communities of less than 150 individuals, being able to routinely follow the curated lives of e.g. 500 people probobly has other effects than a feed of 50 actual RL friends.